swordcloud 0.0.10

Semantic word cloud package for Thai and English

Semantic Word Cloud for Thai and English

swordcloud: A semantic word cloud generator that uses t-SNE and k-means clustering to visualize words in high-dimensional semantic space. Based on A. Mueller's wordcloud module, swordcloud can generate semantic word clouds from Thai and English texts based on any word vector models.

Content

1. Installation
2. Usage
   2.1 Initialize SemanticWordCloud instance
   2.2 Generate from Raw Text
   2.3 Generate from Word Frequencies
   2.4 Generate k-means Cluster Clouds
   2.5 Recolor Words
   2.6 Export Word Clouds
3. Color "Functions"

Installation

swordcloud can be installed using pip:

pip install swordcloud

Optionally, if you want to be able to embed fonts directly into the generated SVGs, an embedfont extra can also be specified:

pip install swordcloud[embedfont]

As of version 0.0.10, the exact list of dependencies is as follow:

• python >= 3.8
• numpy >= 1.21.0
• pillow
• matplotlib >= 1.5.3
• gensim >= 4.0.0
• pandas
• pythainlp >= 3.1.0
• k-means-constrained
• scikit-learn
• (optional) fonttools

Usage

All code below can also be found in the example folder.

Initialize SemanticWordCloud instance

For most use cases, the SemanticWordCloud class is the main API the users will be interacting with.

from swordcloud import SemanticWordCloud
# See the `Color "Functions"` section for detail about these color functions
from swordcloud.color_func import SingleColorFunc

wordcloud = SemanticWordCloud(
    language = 'TH',
    width = 1600,
    height = 800,
    max_font_size = 150,
    prefer_horizontal = 1,
    color_func = SingleColorFunc('black')
)

Please refer to the documentation in src/swordcloud/wordcloud.py or in your IDE for more detail about various options available for customizing the word cloud.

Generate from Raw Text

# Can also be one large string instead of a list of strings
raw_text = list(map(str.strip, open('raw_text.txt', encoding='utf-8')))

wordcloud.generate_from_text(raw_text, random_state=42)

Generate from Word Frequencies

freq = {}
for line in open("word_frequencies.tsv", encoding="utf-8"):
    word, count = line.strip().split('\t')
    freq[word] = int(count)

wordcloud.generate_from_frequencies(freq, random_state=42)

Generate k-means Cluster Clouds

from swordcloud.color_func import FrequencyColorFunc

wordcloud = SemanticWordCloud(
    language = 'TH',
    # make sure the canvas is appropriately large for the number of clusters
    width = 2400,
    height = 1200,
    max_font_size = 150,
    prefer_horizontal = 1
)

wordcloud.generate_from_text(raw_text, kmeans=6, random_state=42, plot_now=False)
# Or directly from `generate_kmeans_cloud` if you already have word frequencies
wordcloud.generate_kmeans_cloud(freq, n_clusters=6, random_state=42, plot_now=False)

# Each sub cloud can then be individually interacted with
# by accessing individual cloud in `sub_clouds` attribute
for cloud, color in zip(wordcloud.sub_clouds, ["red", "blue", "brown", "green", "black", "orange"]):
    cloud.recolor(FrequencyColorFunc(color), plot_now=False)

cloud.show()

Recolor Words

# If the generated colors are not to your liking
# We can recolor them instead of re-generating the whole cloud
from swordcloud.color_func import RandomColorFunc
wordcloud.recolor(RandomColorFunc, random_state=42)

Export Word Clouds

• As pillow's Image

img = wordcloud.to_image()

• As image file

wordcloud.to_file('wordcloud.png')

• As SVG

# Without embedded font
svg = wordcloud.to_svg()
# With embedded font
svg = wordcloud.to_svg(embed_font=True)

# Note that in order to be able to embed fonts
# the `fonttools` package needs to be installed

• As numpy's image array

array = wordcloud.to_array()

Color "Functions"

A number of built-in color "functions" can be accessed from swordcloud.color_func:

from swordcloud.color_func import <your_color_function_here>

The list of available functions is as follow:

• RandomColorFunc (Default)
  Return a random color.
• ColorMapFunc
  Return a random color from the user-specified matplotlib's colormap.
• ImageColorFunc
  Use a user-provided colored image array to determine word color at each position on the canvas.
• SingleColorFunc
  Always return the user-specified color every single time, resulting in every word having the same color.
• ExactColorFunc
  Use a user-provided color dictionary to determine exactly which word should have which color.
• FrequencyColorFunc
  Assign colors based on word frequencies, with less frequent words having lighter colors. The base color is specified by the user.

All the above functions, except RandomColorFunc which cannot be customized further, must be initialized before passing them to the SemanticWordCloud class. For example:

from swordcloud.color_func import ColorMapFunc
color_func = ColorMapFunc("magma")
wordcloud = SemanticWordCloud(
    ...
    color_func = color_func
    ...
)

Users can also implement their own color functions, provided that they are callable with the following signature:

Input:

• word: str
  The word we are coloring
• frequency: float
  Frequency of the word in a scale from 0 to 1
• font_size: int
  Font size of the word
• position: tuple[int, int]
  Coordinate of the top-left point of the word's bounding box on the canvas
• orientation: PIL.Image.Transpose | None
pillow's orientation.
• font_path: str
  Path to the font file (OTF or TFF)
• random_state: random.Random
  Python's random.Random object

Return:
Any object that can be interpreted as a color by pillow. See pillow's documentation for more detail.

Internally, arguments to color functions are always passed as keyword arguments so they can be in any order. However, if your functions only use some of them, make sure to include **kwargs at the end of your function headers so that other arguments do not cause an error.